Uses of Class
io.github.ai4ci.util.Sampler

Packages that use Sampler

Package	Description
io.github.ai4ci.abm
io.github.ai4ci.abm.builders
io.github.ai4ci.abm.inhost
io.github.ai4ci.abm.mechanics
io.github.ai4ci.abm.policy
io.github.ai4ci.config
io.github.ai4ci.util

Uses of Sampler in io.github.ai4ci.abm

Methods in io.github.ai4ci.abm with parameters of type Sampler

Modifier and Type	Method	Description
default boolean	PersonState.isCompliant(Sampler rng)
default boolean	PersonState.isUsingAppToday(Sampler rng)

Uses of Sampler in io.github.ai4ci.abm.builders

Methods in io.github.ai4ci.abm.builders with parameters of type Sampler

Modifier and Type	Method	Description
ImmutableOutbreakBaseline	AgeStratifiedModelBuilder.baselineOutbreak(ImmutableOutbreakBaseline.Builder builder, Outbreak outbreak, Sampler sampler)
ImmutableOutbreakBaseline	DefaultModelBuilder.baselineOutbreak(ImmutableOutbreakBaseline.Builder builder, Outbreak outbreak, Sampler sampler)
default ImmutableOutbreakBaseline	DefaultOutbreakBaseliner.baselineOutbreak(ImmutableOutbreakBaseline.Builder builder, Outbreak outbreak, Sampler sampler)
ImmutablePersonBaseline	AgeStratifiedModelBuilder.baselinePerson(ImmutablePersonBaseline.Builder builder, Person person, Sampler rng)
ImmutablePersonBaseline	DefaultModelBuilder.baselinePerson(ImmutablePersonBaseline.Builder builder, Person person, Sampler rng)
default ImmutablePersonBaseline	DefaultPersonBaseliner.baselinePerson(ImmutablePersonBaseline.Builder builder, Person person, Sampler rng)
default InHostMarkovState	DefaultInHostMarkovStateInitialiser.initialiseInHostModel(MarkovStateModel configuration, ExecutionConfiguration execConfig, Optional<PersonDemographic> person, Sampler rng, int time)
default InHostPhenomenologicalState	DefaultInHostPhenomenologicalStateInitialiser.initialiseInHostModel(PhenomenologicalModel configuration, ExecutionConfiguration execConfig, Optional<PersonDemographic> person, Sampler rng, int time)
default InHostStochasticState	DefaultInHostStochasticStateInitialiser.initialiseInHostModel(StochasticModel configuration, ExecutionConfiguration execConfig, Optional<PersonDemographic> person, Sampler rng, int time)
default <CFG extends InHostConfiguration> InHostModelState<CFG>	DefaultPersonInitialiser.initialiseInHostModel(CFG config, ExecutionConfiguration execConfig, Optional<PersonDemographic> person, Sampler rng, int time)
ImmutableOutbreakState	AgeStratifiedModelBuilder.initialiseOutbreak(ImmutableOutbreakState.Builder builder, Outbreak outbreak, Sampler sampler)
ImmutableOutbreakState	DefaultModelBuilder.initialiseOutbreak(ImmutableOutbreakState.Builder builder, Outbreak outbreak, Sampler sampler)
default ImmutableOutbreakState	DefaultOutbreakInitialiser.initialiseOutbreak(ImmutableOutbreakState.Builder builder, Outbreak outbreak, Sampler sampler)
ImmutablePersonState	AgeStratifiedModelBuilder.initialisePerson(ImmutablePersonState.Builder builder, Person person, Sampler rng)
ImmutablePersonState	DefaultModelBuilder.initialisePerson(ImmutablePersonState.Builder builder, Person person, Sampler rng)
default ImmutablePersonState	DefaultPersonInitialiser.initialisePerson(ImmutablePersonState.Builder builder, Person person, Sampler rng)
ModifiableOutbreak	AgeStratifiedModelBuilder.setupOutbreak(ModifiableOutbreak outbreak, SetupConfiguration config, Sampler sampler)
default ModifiableOutbreak	AgeStratifiedNetworkSetup.setupOutbreak(ModifiableOutbreak outbreak, AgeStratifiedNetworkConfiguration config, Sampler sampler)
ModifiableOutbreak	DefaultModelBuilder.setupOutbreak(ModifiableOutbreak outbreak, SetupConfiguration config, Sampler sampler)
default ModifiableOutbreak	DefaultNetworkSetup.setupOutbreak(ModifiableOutbreak outbreak, WattsStrogatzConfiguration setupConfig, Sampler sampler)

Uses of Sampler in io.github.ai4ci.abm.inhost

Methods in io.github.ai4ci.abm.inhost with parameters of type Sampler

Modifier and Type	Method	Description
static <CFG extends InHostConfiguration> InHostModelState<CFG>	InHostModelState.test(CFG config, ExecutionConfiguration execConfig, Sampler rng)
default InHostMarkovState	InHostMarkovState.update(Sampler sampler, double virionExposure, double immunisationDose)
default InHostModelState<CFG>	InHostModelState.update(Person person, Sampler sampler)
InHostModelState<CFG>	InHostModelState.update(Sampler sampler, double virionExposure, double immunisationDose)
default InHostPhenomenologicalState	InHostPhenomenologicalState.update(Sampler sampler, double virionDose, double immunisationDose)
default InHostStochasticState	InHostStochasticState.update(Sampler rng, double virionDose, double immunisationDose)	Update the viral load for a person.
default InHostMarkovState.DiseaseState	InHostMarkovState.InHostMarkovStateMachine.updateDiseaseState(InHostMarkovState.DiseaseState current, Sampler rng)
default InHostMarkovState.SymptomState	InHostMarkovState.InHostMarkovStateMachine.updateSymptomState(InHostMarkovState.SymptomState current, InHostMarkovState.DiseaseState dx, Sampler rng)

Uses of Sampler in io.github.ai4ci.abm.mechanics

Methods in io.github.ai4ci.abm.mechanics that return types with arguments of type Sampler

Modifier and Type	Method	Description
ModelOperation.TriConsumer<FACTORY,INPUT,Sampler>	ModelOperation.UpdateStage.getConsumer()

Methods in io.github.ai4ci.abm.mechanics with parameters of type Sampler

Modifier and Type	Method	Description
abstract ImmutableOutbreakBaseline	AbstractModelBuilder.baselineOutbreak(ImmutableOutbreakBaseline.Builder builder, Outbreak outbreak, Sampler sampler)
abstract ImmutablePersonBaseline	AbstractModelBuilder.baselinePerson(ImmutablePersonBaseline.Builder builder, Person person, Sampler rng)
ImmutableOutbreakBaseline	AbstractModelBuilder.doBaselineOutbreak(ImmutableOutbreakBaseline.Builder builder, Outbreak o, Sampler sampler)
ImmutablePersonBaseline	AbstractModelBuilder.doBaselinePerson(ImmutablePersonBaseline.Builder builder, Person p, Sampler sampler)
ImmutableOutbreakState	AbstractModelBuilder.doInitialiseOutbreak(ImmutableOutbreakState.Builder builder, Outbreak o, Sampler sampler)
ImmutablePersonState	AbstractModelBuilder.doInitialisePerson(ImmutablePersonState.Builder builder, Person p, Sampler sampler)
ModifiableOutbreak	AbstractModelBuilder.doSetupOutbreak(ModifiableOutbreak outbreak, SetupConfiguration config, Sampler sampler)
default boolean	State.filter(STATE person, StateMachineContext context, Sampler rng)
abstract ImmutableOutbreakState	AbstractModelBuilder.initialiseOutbreak(ImmutableOutbreakState.Builder builder, Outbreak outbreak, Sampler sampler)
abstract ImmutablePersonState	AbstractModelBuilder.initialisePerson(ImmutablePersonState.Builder builder, Person person, Sampler rng)
X	State.nextState(BUILDER builder, STATE current, StateMachineContext context, Sampler rng)	This is the hook for changing the behaviour in the model in terms of mobility, compliance etc in person/behavioural models, or changing system wide properties in outbreak/policy models.
void	StateMachine.performHistoryUpdate(ImmutableOutbreakHistory.Builder builder, OutbreakState outbreak, Sampler rng)
void	StateMachine.performHistoryUpdate(ImmutablePersonHistory.Builder builder, PersonState person, Sampler rng)
void	StateMachine.performStateUpdate(ImmutableOutbreakState.Builder builder, OutbreakState outbreak, Sampler rng)
void	StateMachine.performStateUpdate(ImmutablePersonState.Builder builder, PersonState person, Sampler rng)	Update the state machine and store the new state
double	Abstraction.Distribution.sample(Sampler rng)
abstract ModifiableOutbreak	AbstractModelBuilder.setupOutbreak(ModifiableOutbreak outbreak, SetupConfiguration config, Sampler sampler)
void	State.updateHistory(HISTORY builder, STATE current, StateMachineContext context, Sampler rng)	Depending on the type this updated the outbreak or person history, and this is the hook for changing the testing, or adding in contacts / exposures if we are looking at people.
default void	StateUtils.DefaultNoTesting.updateHistory(ImmutablePersonHistory.Builder builder, PersonState person, StateMachineContext context, Sampler rng)
default void	StateUtils.DoesPCRIfSymptomatic.updateHistory(ImmutablePersonHistory.Builder builder, PersonState person, StateMachineContext context, Sampler rng)

Method parameters in io.github.ai4ci.abm.mechanics with type arguments of type Sampler

Modifier and Type	Method	Description
static ModelOperation.OutbreakStateUpdater	ModelOperation.updateOutbreakState(Predicate<Outbreak> selector, ModelOperation.TriConsumer<ImmutableOutbreakState.Builder,Outbreak,Sampler> consumer)
static ModelOperation.PersonStateUpdater	ModelOperation.updatePersonState(Predicate<Person> selector, ModelOperation.TriConsumer<ImmutablePersonState.Builder,Person,Sampler> consumer)
Updater	Updater.withOutbreakProcessor(Predicate<Outbreak> test, ModelOperation.TriConsumer<ImmutableOutbreakState.Builder,Outbreak,Sampler> updater)
Updater	Updater.withPersonProcessor(Predicate<Person> test, ModelOperation.TriConsumer<ImmutablePersonState.Builder,Person,Sampler> updater)

Uses of Sampler in io.github.ai4ci.abm.policy

Methods in io.github.ai4ci.abm.policy with parameters of type Sampler

Modifier and Type	Method	Description
default void	PolicyModel.updateHistory(ImmutableOutbreakHistory.Builder builder, OutbreakState person, StateMachineContext context, Sampler rng)

Uses of Sampler in io.github.ai4ci.config

Methods in io.github.ai4ci.config with parameters of type Sampler

Modifier and Type	Method	Description
static double	TestParameters.applyNoise(double normalisedSignal, double sensitivity, double specificity, double limitOfDetection, Sampler rng)	This comes from the following logic. a uniform distribution of noise is added.
default double	TestParameters.applyNoise(double normViralLoad, Sampler rng)

Uses of Sampler in io.github.ai4ci.util

Methods in io.github.ai4ci.util that return Sampler

Modifier and Type	Method	Description
static Sampler	Sampler.getSampler()
static Sampler	Sampler.getSampler(String urn)
Sampler	Sampler.withSeed(String urn)

Methods in io.github.ai4ci.util with parameters of type Sampler

Modifier and Type	Method	Description
default double	EmpiricalDistribution.sample(Sampler rng)
default double	HistogramDistribution.sample(Sampler rng)
default double	ResampledDistribution.sample(Sampler rng)
default double	SimpleDistribution.sample(Sampler rng)
default double	TransformedDistribution.sample(Sampler rng)